Rubrex is the measurement layer for AI. We define what good means for your model, then run the feedback loop that gets it there.
“Most AI teams can't agree on what a good output looks like. Retraining cycles run without clear signal. Quality is discussed in opinions, not numbers.”
Teams that don't yet have a measurement system
We define what good means for your model, build the rubrics, establish scoring criteria, and create the evaluation infrastructure your team can actually use.
You stop arguing about which model version is actually better.
Teams that know what to evaluate and need it done
We deploy trained evaluators, run preference comparisons and output grading, and deliver structured datasets aligned to your retraining schedule.
You ship the next training cycle without standing up a labeling team.
We understand your model, use case, and where quality breaks down.
We build your evaluation framework or align our execution to your existing one.
We run the feedback pipeline and deliver structured, training-ready data.
We work with AI teams that take model quality seriously. If you're training, fine-tuning, or post-training a model and need a structured way to measure and improve it — that's us.
No lab ties, no model-vendor allegiance, no conflict of interest. Our evaluations answer to your model and nothing else.
Our methods come from running evaluation design, rubric calibration, and RLHF pipelines across frontier models.
We don't label at random. Every engagement starts with a structured definition of quality, tied to a metric.
Senior oversight on every engagement. You work with people who understand evaluation, not an anonymous crowd platform.
WE'LL RESPOND WITHIN 24 HOURS.